The real work in moving AI from PoC to production lies in the surrounding system
The article argues that a proof of concept only proves the model can produce correct answers, while production requires exception handling, human review, monitoring, access control, fallback paths, and a named owner with support. It cites surveys showing many AI projects fail because this surrounding infrastructure is underestimated. The piece emphasizes that production must safely handle wrong answers at scale, not just succeed on sample data.
The article's central claim is that the model itself is rarely the bottleneck in enterprise AI deployment. Survey data cited shows a wide gap between adoption ambitions and operational readiness: while most organizations expect to deploy agentic AI within two years, only about one in five has mature governance in place. The RAND study's finding that AI projects fail at roughly double the rate of conventional IT efforts underscores how often the surrounding infrastructure is neglected.
The invoice automation example illustrates the point — strong accuracy metrics (88% straight-through processing) still weren't sufficient because integration, review workflows, and permissions remained unbuilt. The eight production requirements listed — exception handling, human review, test sets, monitoring, access control, fallback paths, ownership, and support — constitute the actual engineering effort, not the model itself.
The article suggests organizations may be overestimating how quickly AI can deliver value. If surrounding infrastructure — review steps, monitoring, fallback paths — is consistently underestimated, businesses could deploy systems that fail in unexpected ways, eroding trust in AI more broadly. Workers in roles that review AI output may see their responsibilities shift toward exception handling rather than routine processing. The emphasis on named ownership and support arrangements implies accountability structures will need to mature alongside the technology, potentially reshaping how teams are staffed and budgets allocated.